Journal of System Simulation
Abstract
Abstract: A multi-modality modeling method for time series data based on fuzzy cognitive maps is proposed to address the problem that a single model is difficult to accurately reflect the multi-modal characteristics of time series.The bootstrap method is used to select multiple sub-sequences from the original time serieswhich contain the diverse modality in the original time series. The fuzzy cognitive map sub-models are constructed on each sub-sequencesrespectively. The formed sub-models are further merged by means of granular computing method and the merging performance with different weighting strategies is analyzed. The developed multi-modal model not only has prediction abilities at the numeric and interval level, but also has better interpretability. Experimental results on public datasets exhibit the usefulness and satisfactory efficiency of the proposed approach.
Recommended Citation
Feng, Guoliang; Lu, Wei; and Yang, Jianhua
(2022)
"Modeling Time Series Using Multi-Modality Fuzzy Cognitive Maps,"
Journal of System Simulation: Vol. 34:
Iss.
3, Article 12.
DOI: 10.16182/j.issn1004731x.joss.20-0834
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol34/iss3/12
First Page
543
Revised Date
2021-01-09
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-0834
Last Page
554
CLC
TP274
Recommended Citation
Guoliang Feng, Wei Lu, Jianhua Yang. Modeling Time Series Using Multi-Modality Fuzzy Cognitive Maps[J]. Journal of System Simulation, 2022, 34(3): 543-554.
DOI
10.16182/j.issn1004731x.joss.20-0834
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